1 00:00:00,120 --> 00:00:10,440 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is the Bloomberg 2 00:00:10,520 --> 00:00:13,720 Speaker 1: Daybreak Asia podcast. I'm Doug Krisner. You can join Brian 3 00:00:13,800 --> 00:00:16,640 Speaker 1: Curtis and myself for the stories, making news and moving 4 00:00:16,680 --> 00:00:19,560 Speaker 1: markets in the APAC region. You can subscribe to the 5 00:00:19,600 --> 00:00:23,080 Speaker 1: show anywhere you get your podcast and always on Bloomberg Radio, 6 00:00:23,320 --> 00:00:26,080 Speaker 1: the Bloomberg Terminal, and the Bloomberg Business App. 7 00:00:27,440 --> 00:00:31,240 Speaker 2: Nvidia has unveiled new chips aimed at extending the company's 8 00:00:31,240 --> 00:00:35,400 Speaker 2: dominance in artificial intelligence computing. And Vidia says its new 9 00:00:35,440 --> 00:00:39,839 Speaker 2: processor design, named Blackwell, is multiple times faster at handling 10 00:00:39,880 --> 00:00:43,640 Speaker 2: the computer models behind AI. That includes the process of 11 00:00:43,720 --> 00:00:48,120 Speaker 2: developing AI technology known as training and running the process AI. 12 00:00:48,720 --> 00:00:51,320 Speaker 2: And Vidia says it's Blackwell chips will be the basis 13 00:00:51,360 --> 00:00:54,280 Speaker 2: of new computers and other products being deployed by the 14 00:00:54,280 --> 00:00:59,240 Speaker 2: world's largest data center operators. That roster includes Amazon, Microsoft, 15 00:00:59,320 --> 00:01:03,360 Speaker 2: Alphabet and Oracle. Jensen Hoong and Video CEO and co 16 00:01:03,440 --> 00:01:07,000 Speaker 2: founders said AI is the driving force in a fundamental 17 00:01:07,080 --> 00:01:08,160 Speaker 2: change in the economy. 18 00:01:08,680 --> 00:01:12,679 Speaker 3: The industry is being transformed, not just ours because the 19 00:01:12,720 --> 00:01:17,400 Speaker 3: computer industry. The computer is the single most important instrument 20 00:01:17,440 --> 00:01:23,640 Speaker 3: of society today. Fundamental transformations and computing affects every industry. 21 00:01:24,840 --> 00:01:28,160 Speaker 2: Jensen Hoong there and Vidia CEO and co founder and 22 00:01:28,240 --> 00:01:31,480 Speaker 2: Vidia shriffes we're down about one point seven percent in 23 00:01:31,560 --> 00:01:34,360 Speaker 2: late trading. Joining us now for some discussion on this 24 00:01:34,440 --> 00:01:39,040 Speaker 2: is Nick Turner, Bloomberg, Senior editor for the Technology Team. Nick, 25 00:01:39,080 --> 00:01:41,440 Speaker 2: thank you for joining us. So Jensen Wong, we heard 26 00:01:41,520 --> 00:01:45,240 Speaker 2: him saying that the Blackwell processor is multiple times faster 27 00:01:45,440 --> 00:01:49,960 Speaker 2: than previous chips. That sounds impressive, of course, but with 28 00:01:50,040 --> 00:01:53,480 Speaker 2: a company like Nvidia, you know, there's all kinds of superlatives. 29 00:01:54,040 --> 00:01:56,400 Speaker 2: And we see that the stock price in after hours 30 00:01:56,480 --> 00:01:58,920 Speaker 2: was a little bit underwhelming. How is the response in 31 00:01:58,960 --> 00:01:59,320 Speaker 2: the room? 32 00:02:01,040 --> 00:02:05,240 Speaker 4: Well, I mean, this is their first in person event 33 00:02:05,360 --> 00:02:09,320 Speaker 4: for this GtC conference in four years, essentially since the 34 00:02:09,320 --> 00:02:12,760 Speaker 4: pandemic started, and you know, I wasn't there myself, but 35 00:02:12,880 --> 00:02:14,800 Speaker 4: just seeing the photos of my colleagues end and just 36 00:02:14,840 --> 00:02:17,200 Speaker 4: the vibe of people who are there, it just really 37 00:02:17,240 --> 00:02:23,639 Speaker 4: seemed like kind of a really pumped up crowd. And yeah, 38 00:02:23,680 --> 00:02:26,799 Speaker 4: I mean this company is now one of the biggest 39 00:02:26,800 --> 00:02:29,960 Speaker 4: stars in the tech world, up there with Apple and Microsoft, 40 00:02:30,040 --> 00:02:32,720 Speaker 4: and in some ways more exciting to people just because 41 00:02:32,720 --> 00:02:37,359 Speaker 4: of what it's doing. In terms of changing the way 42 00:02:37,400 --> 00:02:38,919 Speaker 4: computing works. 43 00:02:38,880 --> 00:02:42,280 Speaker 1: And the company seems so far ahead of the competition 44 00:02:42,360 --> 00:02:44,160 Speaker 1: when it comes to this, and I think it goes 45 00:02:44,200 --> 00:02:48,400 Speaker 1: back to its roots in developing the graphics processors that 46 00:02:48,440 --> 00:02:52,200 Speaker 1: were used for the gaming industry, where you had to 47 00:02:52,280 --> 00:02:55,640 Speaker 1: kind of create a chip that was able to handle 48 00:02:55,760 --> 00:03:00,079 Speaker 1: complex calculations so quickly but dynamically as well, because you 49 00:03:00,120 --> 00:03:03,880 Speaker 1: had inputs coming from two sides, from the game itself 50 00:03:03,880 --> 00:03:07,840 Speaker 1: and then from the gamer who was interacting with the 51 00:03:07,880 --> 00:03:10,960 Speaker 1: control surface. I'm wondering, though, when it comes to this 52 00:03:11,040 --> 00:03:15,400 Speaker 1: idea of encryption. We played a cut earlier where mister 53 00:03:15,480 --> 00:03:18,680 Speaker 1: Wong was talking about encryption. How do we understand what 54 00:03:18,919 --> 00:03:22,840 Speaker 1: Nvidia is doing as it moves toward AI to develop 55 00:03:23,120 --> 00:03:24,280 Speaker 1: a secure environment. 56 00:03:25,960 --> 00:03:28,679 Speaker 4: Well, I mean, I think, first of all, this is 57 00:03:28,720 --> 00:03:31,120 Speaker 4: a company really that has its hands in so many 58 00:03:31,160 --> 00:03:35,800 Speaker 4: areas in terms of just designing software services, the simulations. 59 00:03:35,800 --> 00:03:38,800 Speaker 4: Obviously the chips are kind of its biggest source of revenue, 60 00:03:38,840 --> 00:03:41,440 Speaker 4: but I think that it really wants to sort of 61 00:03:41,440 --> 00:03:44,920 Speaker 4: create an ecosystem around what it's selling, and obviously that 62 00:03:44,960 --> 00:03:51,040 Speaker 4: includes security and encryption. And I think that it, I mean, 63 00:03:51,120 --> 00:03:53,720 Speaker 4: in broader terms, a lot of these areas that it's 64 00:03:53,760 --> 00:03:56,360 Speaker 4: been in. It's sort of had to make sure people 65 00:03:56,360 --> 00:03:59,160 Speaker 4: feel like they're safe and they're you know, secure, and 66 00:03:59,520 --> 00:04:02,800 Speaker 4: I think that that, you know, that's part of the pitch. 67 00:04:04,840 --> 00:04:08,160 Speaker 2: It is interesting. It's I think we're learning here as 68 00:04:08,200 --> 00:04:11,200 Speaker 2: time goes on that there's not just one winner in 69 00:04:11,240 --> 00:04:14,840 Speaker 2: this AI space, It'll be multiple winners. So in addition 70 00:04:14,960 --> 00:04:17,080 Speaker 2: to in video, a lot of the people in that 71 00:04:17,160 --> 00:04:20,799 Speaker 2: room probably you know, working we said, Amazon, Microsoft, Alphabet 72 00:04:20,800 --> 00:04:25,320 Speaker 2: in Oracle are in a sense even competitors with in video. 73 00:04:25,360 --> 00:04:28,080 Speaker 2: And then you have a MD and Broadcommon a whole 74 00:04:28,120 --> 00:04:32,919 Speaker 2: slew of others, you know, in your estimation here is 75 00:04:32,920 --> 00:04:35,960 Speaker 2: is this rolling out in that way where a lot 76 00:04:36,000 --> 00:04:38,240 Speaker 2: of people win? And that's not even to mention all 77 00:04:38,279 --> 00:04:42,480 Speaker 2: the downstream recipients of the AI technology. Are we going 78 00:04:42,520 --> 00:04:43,680 Speaker 2: to see a lot of winners here? 79 00:04:44,640 --> 00:04:47,760 Speaker 4: What demand is so much higher than supply at the 80 00:04:47,880 --> 00:04:50,240 Speaker 4: moment that I think in the short term release, it 81 00:04:50,279 --> 00:04:51,600 Speaker 4: does seem like you're gonna have a lot of winners. 82 00:04:51,600 --> 00:04:53,760 Speaker 4: I mean, I think am D is going to be 83 00:04:53,800 --> 00:04:57,280 Speaker 4: successful with it's you know, the same sort of styles 84 00:04:57,320 --> 00:04:59,479 Speaker 4: chip that in video sells that's coming out this year. 85 00:05:00,400 --> 00:05:03,720 Speaker 4: It's already increased its forecast for how much that's going 86 00:05:03,800 --> 00:05:07,839 Speaker 4: to generate. Intel has its own version too, which is 87 00:05:07,839 --> 00:05:12,440 Speaker 4: maybe not being received quite as warmly. But then, as 88 00:05:12,480 --> 00:05:15,440 Speaker 4: you mentioned, the cloud companies themselves, they're designing some of 89 00:05:15,480 --> 00:05:18,360 Speaker 4: their own chips that are going to handle these functions, 90 00:05:18,400 --> 00:05:21,960 Speaker 4: so ultimately they become customers but also competitors. But part 91 00:05:22,000 --> 00:05:23,520 Speaker 4: of the reason for that is they really want to 92 00:05:23,560 --> 00:05:26,600 Speaker 4: sort of have control over the process and maybe make 93 00:05:26,640 --> 00:05:28,960 Speaker 4: it a little bit easier to ensure that they get 94 00:05:29,000 --> 00:05:31,320 Speaker 4: as much of whatever chip it is that they need. 95 00:05:31,920 --> 00:05:34,599 Speaker 1: Are we reaching an inflection point in the near future 96 00:05:34,640 --> 00:05:38,080 Speaker 1: where this investment in the technology is going to have 97 00:05:38,160 --> 00:05:40,520 Speaker 1: to be justified. There's going to have to be a 98 00:05:40,600 --> 00:05:44,080 Speaker 1: return on investment seen, so the use case, I mean, 99 00:05:44,160 --> 00:05:46,920 Speaker 1: I understand the technology and the amazing things that it's 100 00:05:47,000 --> 00:05:50,320 Speaker 1: capable of doing, but for a company to actually derive 101 00:05:50,360 --> 00:05:54,360 Speaker 1: a benefit from that as it's invested in this technology. 102 00:05:54,400 --> 00:05:56,159 Speaker 1: Are we always away from seeing that? 103 00:05:57,480 --> 00:05:59,680 Speaker 4: Well, it's funny to hear him speak today. He did 104 00:05:59,760 --> 00:06:01,600 Speaker 4: kind of He kept coming back to this theme of 105 00:06:01,640 --> 00:06:05,279 Speaker 4: sort of the the AI factory, that these data centers 106 00:06:05,279 --> 00:06:07,480 Speaker 4: are sort of AI factories, and I think he sort 107 00:06:07,520 --> 00:06:10,800 Speaker 4: of said he talked about how they were money makers, improfitable, 108 00:06:10,839 --> 00:06:13,320 Speaker 4: and I don't know if that's the case right now 109 00:06:13,360 --> 00:06:16,280 Speaker 4: for these companies, but I think that it's it's It 110 00:06:16,320 --> 00:06:19,120 Speaker 4: did seem like he was hitting the theme that these 111 00:06:19,160 --> 00:06:23,200 Speaker 4: things were going to be a profitable enterprise, at least 112 00:06:23,279 --> 00:06:25,720 Speaker 4: in the near term. But I think you're right. If 113 00:06:26,000 --> 00:06:31,760 Speaker 4: suddenly tomorrow AI services were held to you know, sort 114 00:06:31,800 --> 00:06:34,840 Speaker 4: of a make or break you have to be profitable 115 00:06:34,960 --> 00:06:38,000 Speaker 4: or sort of cutting you off, then that wouldn't be 116 00:06:38,000 --> 00:06:41,200 Speaker 4: a I don't think any many are already at that point, 117 00:06:42,279 --> 00:06:44,359 Speaker 4: even the big companies that are investing in this. So 118 00:06:45,440 --> 00:06:48,839 Speaker 4: it's the big debate I think is is this closer 119 00:06:48,880 --> 00:06:51,600 Speaker 4: to the dot com bubble of two thousand year two 120 00:06:51,600 --> 00:06:55,200 Speaker 4: thousand or is it farther back like nineteen ninety five 121 00:06:55,400 --> 00:06:57,720 Speaker 4: or something like that. I mean, you hear that a lot, and. 122 00:06:58,440 --> 00:07:02,320 Speaker 2: One is that in the stock price. And the reason 123 00:07:02,360 --> 00:07:04,880 Speaker 2: I asked you about are there multiple winners is so 124 00:07:04,920 --> 00:07:10,600 Speaker 2: that people don't necessarily feel so, you know, beholden by Nvidia, 125 00:07:10,680 --> 00:07:13,000 Speaker 2: that there are other places to turn. So I wanted 126 00:07:13,000 --> 00:07:14,640 Speaker 2: to ask you about this process. I mean, he said 127 00:07:14,760 --> 00:07:20,080 Speaker 2: multiple times faster, so that's difficult to actually you know, 128 00:07:20,440 --> 00:07:24,440 Speaker 2: deal with quantitatively, how does that compare with the last 129 00:07:24,520 --> 00:07:27,040 Speaker 2: chip that they rolled out? How much did they say 130 00:07:27,400 --> 00:07:30,800 Speaker 2: it was faster than previous iterations. So in order to 131 00:07:30,800 --> 00:07:32,640 Speaker 2: get a feel for you know, how far is. 132 00:07:32,680 --> 00:07:36,560 Speaker 4: Nvidia, I had yeah, I mean it, you know, not 133 00:07:36,640 --> 00:07:38,520 Speaker 4: to get into sort of the two nitty gritty of 134 00:07:38,520 --> 00:07:39,720 Speaker 4: the numbers, but I mean, you looked at some of 135 00:07:39,720 --> 00:07:41,600 Speaker 4: these charts he threw up. Then it's really a dramatic 136 00:07:42,080 --> 00:07:44,640 Speaker 4: gain from even the you know, the current ship that 137 00:07:44,720 --> 00:07:48,800 Speaker 4: obviously is so far ahead of the competition already that 138 00:07:49,200 --> 00:07:51,680 Speaker 4: it's you know, in this incredible demand. So I think 139 00:07:51,720 --> 00:07:55,760 Speaker 4: that that it is going to be you know both 140 00:07:55,840 --> 00:07:58,440 Speaker 4: I think, more powerful but also hopefully more power efficient, 141 00:07:58,880 --> 00:08:01,760 Speaker 4: which has been another issue that the AI industry in 142 00:08:01,800 --> 00:08:04,520 Speaker 4: general and video has hit up against. It just that 143 00:08:05,120 --> 00:08:08,960 Speaker 4: these things, you know, the electricity required of these things, 144 00:08:08,960 --> 00:08:12,360 Speaker 4: the fact that data centers are outstripping the amount of 145 00:08:12,920 --> 00:08:15,880 Speaker 4: energy available in their communities in some cases. I mean, 146 00:08:16,160 --> 00:08:18,120 Speaker 4: that's another He's got to hit a lot of points 147 00:08:18,400 --> 00:08:21,360 Speaker 4: to sort of make sure people are not growing concerned 148 00:08:21,360 --> 00:08:22,320 Speaker 4: about some of these issues. 149 00:08:22,560 --> 00:08:25,600 Speaker 1: So when in video is talking about the application for AI, 150 00:08:25,680 --> 00:08:27,560 Speaker 1: the focus seems to be the cloud. And I want 151 00:08:27,600 --> 00:08:29,680 Speaker 1: to see if we can kind of shoehorn in the 152 00:08:29,720 --> 00:08:32,960 Speaker 1: Apple story today, because it seems like what Apple is 153 00:08:33,000 --> 00:08:35,200 Speaker 1: trying to do is to give a lot more of 154 00:08:35,200 --> 00:08:39,400 Speaker 1: that computing power to the phone where the user is 155 00:08:39,400 --> 00:08:43,000 Speaker 1: actually interfacing with the phone and maybe not so reliant 156 00:08:43,000 --> 00:08:45,120 Speaker 1: on what's happening in the cloud. Is that a fair statement? 157 00:08:46,160 --> 00:08:49,160 Speaker 4: Yes, I mean, I think Apple in general handling things 158 00:08:49,160 --> 00:08:53,120 Speaker 4: on the device, it's better for security, privacy. That's a 159 00:08:53,200 --> 00:08:57,040 Speaker 4: huge issue in terms of the marketing Apple, and I 160 00:08:57,080 --> 00:09:00,160 Speaker 4: think that the problem they're finding themselves in is that 161 00:09:00,200 --> 00:09:02,360 Speaker 4: they're just a little bit too far behind on some 162 00:09:02,480 --> 00:09:06,600 Speaker 4: of the cloud based services that people have gotten excited about, 163 00:09:06,520 --> 00:09:10,680 Speaker 4: a chat, GPT and you know that ILK, obviously Gemini, 164 00:09:10,720 --> 00:09:12,640 Speaker 4: which is the one they're in talks with from Google 165 00:09:12,679 --> 00:09:15,160 Speaker 4: to potentially use in the iPhone. So I think they 166 00:09:15,200 --> 00:09:17,600 Speaker 4: find that that they've been concentrated on this on device 167 00:09:17,640 --> 00:09:22,199 Speaker 4: stuff for good reason perhaps, but they're they're not quite 168 00:09:22,200 --> 00:09:26,000 Speaker 4: cut up on the cloud based AI services. 169 00:09:26,760 --> 00:09:28,679 Speaker 2: All right, Nick, thanks very much for joining us. A 170 00:09:28,840 --> 00:09:32,200 Speaker 2: very interesting material there, Nick Turner Bloomberg, Senior editor for 171 00:09:32,280 --> 00:09:35,600 Speaker 2: the Tech Team. So let's see, we've got Amazon Anthropic, 172 00:09:35,679 --> 00:09:39,080 Speaker 2: We've got Microsoft, Open Ai and now Alphabet Apple. At 173 00:09:39,160 --> 00:09:43,520 Speaker 2: least those three big camps, Nvidia and a MD seemingly 174 00:09:43,640 --> 00:09:47,320 Speaker 2: in their two camps on the chip side, it's setting 175 00:09:47,400 --> 00:09:57,559 Speaker 2: up as a fascinating story. Chinese regulators say the developer 176 00:09:57,679 --> 00:10:01,760 Speaker 2: Evergrand falsely inflated revenue by more than seventy eight billion 177 00:10:01,800 --> 00:10:04,920 Speaker 2: dollars in the two years leading to its failure. Joining 178 00:10:05,000 --> 00:10:08,240 Speaker 2: US now is Carrie Sue Lindberg Bloomberg Private Credit and 179 00:10:08,360 --> 00:10:12,040 Speaker 2: Loans reporter. So we won't have any trading in Evergrand 180 00:10:12,080 --> 00:10:16,680 Speaker 2: stocks bend suspended since it went into liquidation. It's a 181 00:10:16,720 --> 00:10:19,480 Speaker 2: good I guess it's a good time today to talk 182 00:10:19,480 --> 00:10:23,480 Speaker 2: a little bit about the overarching story about Evergrand. In 183 00:10:23,600 --> 00:10:27,280 Speaker 2: terms of these finds, a hefty fine for the founder 184 00:10:27,360 --> 00:10:31,520 Speaker 2: and the former CEO as well as for the hung 185 00:10:31,640 --> 00:10:33,160 Speaker 2: Da unit in China. 186 00:10:33,240 --> 00:10:38,240 Speaker 5: Right, that's right, So thanks Brian. So essentially Huy who 187 00:10:38,400 --> 00:10:41,079 Speaker 5: was kind of the kind of the found the founder 188 00:10:41,280 --> 00:10:45,200 Speaker 5: of the founder and chairman of Evergrand, was fined forty 189 00:10:45,280 --> 00:10:49,240 Speaker 5: seven million yun as well as Hung Da was fined 190 00:10:49,440 --> 00:10:53,240 Speaker 5: seven four point one eight billion un which is kind 191 00:10:53,240 --> 00:10:56,600 Speaker 5: of quite sizable fins And so really what this means 192 00:10:56,640 --> 00:10:59,400 Speaker 5: is in some ways. This is the latest blow for Huay, 193 00:10:59,600 --> 00:11:02,640 Speaker 5: who was once among Asia's richest tycoons and saw kind 194 00:11:02,640 --> 00:11:06,720 Speaker 5: of the sprawling empire. So, I mean, it really strikes 195 00:11:06,760 --> 00:11:09,760 Speaker 5: at the heart of Evergrand, especially when they were once 196 00:11:09,880 --> 00:11:12,320 Speaker 5: kind of the one of the leading growth engine of 197 00:11:13,280 --> 00:11:16,600 Speaker 5: the world's second largest economy. And this is really just 198 00:11:16,880 --> 00:11:20,880 Speaker 5: you know, more details and kind of a flaming the fires. 199 00:11:20,880 --> 00:11:24,840 Speaker 1: In that sense, I'm wondering whether it's unique to Evergrand, 200 00:11:24,880 --> 00:11:27,320 Speaker 1: which is not to say that other companies were, you know, 201 00:11:27,640 --> 00:11:31,679 Speaker 1: engaged in similar practices and maybe not as extreme as 202 00:11:31,679 --> 00:11:34,160 Speaker 1: this where you inflate revenue by more than seventy eight 203 00:11:34,160 --> 00:11:37,720 Speaker 1: billion dollars over two years. But are there questions being 204 00:11:37,880 --> 00:11:42,120 Speaker 1: asked more broadly about the accounting standards that some of 205 00:11:42,160 --> 00:11:45,480 Speaker 1: these property developers have used in the past, or ways 206 00:11:45,480 --> 00:11:48,079 Speaker 1: that they are accounting for property sales. 207 00:11:49,320 --> 00:11:50,880 Speaker 5: Yeah, I mean, that's a good point. I mean, And 208 00:11:51,000 --> 00:11:53,160 Speaker 5: just to put a little bit into perspective, the numbers 209 00:11:53,160 --> 00:11:57,200 Speaker 5: you brought, so for twenty nineteen they inflated their Hangdah 210 00:11:57,360 --> 00:12:00,600 Speaker 5: inflated their tour revenue by sixty three percent, and for 211 00:12:00,679 --> 00:12:04,200 Speaker 5: twenty twenty, they inflated their numbers by eighty seven percent, 212 00:12:04,280 --> 00:12:07,440 Speaker 5: which is massive. Well, I don't think this is in 213 00:12:07,480 --> 00:12:10,280 Speaker 5: some ways unique to Evergrand. I think what it really 214 00:12:10,280 --> 00:12:12,920 Speaker 5: shows you is why the government was trying to kind 215 00:12:12,920 --> 00:12:17,199 Speaker 5: of overhaul the whole regulatory system quite strongly and kind 216 00:12:17,200 --> 00:12:20,319 Speaker 5: of tighten the lending. So if we'll remember and kind 217 00:12:20,320 --> 00:12:22,560 Speaker 5: of just going back a little bit onto kind of 218 00:12:22,559 --> 00:12:26,320 Speaker 5: the three plus year saga of this company, it was 219 00:12:26,440 --> 00:12:30,720 Speaker 5: really you know, it was expanding tremendously by kind of 220 00:12:31,160 --> 00:12:36,000 Speaker 5: taking on massive amounts of debt. But once the regulator 221 00:12:36,040 --> 00:12:39,760 Speaker 5: started tightening their ability to borrow money, that's when it's 222 00:12:40,000 --> 00:12:42,160 Speaker 5: that's when the trouble really started. And so I think 223 00:12:42,360 --> 00:12:45,679 Speaker 5: that's really what we're seeing, right, We're seeing, you know, 224 00:12:45,720 --> 00:12:48,760 Speaker 5: once you tighten those practices, if you're you're not one 225 00:12:48,840 --> 00:12:51,720 Speaker 5: hundred percent sound in some ways that this is what 226 00:12:51,920 --> 00:12:54,560 Speaker 5: could await you. And I think that's really a signal 227 00:12:54,600 --> 00:12:55,520 Speaker 5: to other companies. 228 00:12:56,880 --> 00:13:00,280 Speaker 2: Yeah, and Andhay himself is still under police control as 229 00:13:00,320 --> 00:13:04,079 Speaker 2: far as we know, and his subject to whatever mandatory 230 00:13:04,120 --> 00:13:09,040 Speaker 2: measures means. But it is you know, is there much 231 00:13:09,160 --> 00:13:13,840 Speaker 2: progress in terms of selling assets of Evergrand to pay 232 00:13:13,880 --> 00:13:17,040 Speaker 2: off the you know, the creditors that because that's an 233 00:13:17,080 --> 00:13:20,360 Speaker 2: ongoing story that I think investors would be quite interested in. 234 00:13:20,920 --> 00:13:23,400 Speaker 5: You're right, it is an ongoing story because you know, 235 00:13:23,640 --> 00:13:27,280 Speaker 5: just in January, right they were the group received a 236 00:13:27,320 --> 00:13:29,640 Speaker 5: liquidation order, and so I think that's still kind of 237 00:13:29,679 --> 00:13:33,920 Speaker 5: the ongoing story. I've no news on that front, but 238 00:13:33,960 --> 00:13:37,400 Speaker 5: I could say that the mansion of Hong Kong Mansion 239 00:13:37,480 --> 00:13:41,359 Speaker 5: that's tied to Howay, who was the founder of Evergrand, 240 00:13:41,440 --> 00:13:44,840 Speaker 5: has been put up for sale. It's a luxury block 241 00:13:44,960 --> 00:13:48,520 Speaker 5: that and the tender ends on April twenty second, So 242 00:13:48,840 --> 00:13:51,240 Speaker 5: I mean that's also kind of a good barometer who 243 00:13:51,320 --> 00:13:53,760 Speaker 5: wants to buy that and where does the proceeds for 244 00:13:53,960 --> 00:13:58,200 Speaker 5: that his personal asset go. I think that could provide 245 00:13:58,320 --> 00:14:01,880 Speaker 5: some a bell weather for where everything else goes as well. Well. 246 00:14:01,880 --> 00:14:03,640 Speaker 1: That was the question that I was going to ask 247 00:14:03,960 --> 00:14:07,160 Speaker 1: next about the government's ability to claw back any of 248 00:14:07,160 --> 00:14:12,040 Speaker 1: this inflated this overstatement. Are there means for the government 249 00:14:12,080 --> 00:14:14,520 Speaker 1: to be able to try to do that, to seize 250 00:14:14,559 --> 00:14:16,480 Speaker 1: assets to make up for this? 251 00:14:18,120 --> 00:14:20,280 Speaker 5: There are, But I think a large part of the 252 00:14:20,320 --> 00:14:24,600 Speaker 5: issue is that not all of Evergrend's buildings and real 253 00:14:24,720 --> 00:14:29,360 Speaker 5: estate infrastructure that they were responsible for were completed. So 254 00:14:30,400 --> 00:14:32,480 Speaker 5: I mean you could seize an acid. But what's the 255 00:14:32,520 --> 00:14:34,600 Speaker 5: point of seizing an acid that's not completed? 256 00:14:34,720 --> 00:14:34,920 Speaker 4: Right? 257 00:14:35,160 --> 00:14:38,040 Speaker 5: So I think that's also the really tricky issue that's 258 00:14:38,080 --> 00:14:40,520 Speaker 5: not facing not just evergrand i'd say, but kind of 259 00:14:40,560 --> 00:14:44,960 Speaker 5: the slew of Chinese real estate companies that are under 260 00:14:45,040 --> 00:14:47,240 Speaker 5: severe distress or have already defaulted. 261 00:14:48,440 --> 00:14:50,640 Speaker 2: So carry see. One of the stories we did was that, 262 00:14:50,720 --> 00:14:53,320 Speaker 2: you know, we did see investment pick up and industrial 263 00:14:53,360 --> 00:14:56,120 Speaker 2: production pick up, and we just mentioned a few moments 264 00:14:56,160 --> 00:15:00,600 Speaker 2: ago that it has led some analysts and vesters to 265 00:15:00,600 --> 00:15:02,640 Speaker 2: think that maybe we'll get a little bit less stimulus. 266 00:15:02,840 --> 00:15:05,680 Speaker 2: It brings us to the property sector. That's the biggest overhang. 267 00:15:06,680 --> 00:15:09,200 Speaker 2: What are we seeing in terms of sales now, transactions 268 00:15:09,240 --> 00:15:12,240 Speaker 2: and prices? Is it getting better or is it still 269 00:15:12,280 --> 00:15:12,880 Speaker 2: getting worse? 270 00:15:13,920 --> 00:15:17,120 Speaker 5: I think it's not great, to be honest, and that's 271 00:15:17,160 --> 00:15:19,400 Speaker 5: really the key barometer what a lot of people are 272 00:15:19,400 --> 00:15:23,360 Speaker 5: looking out for. We'll wait that news as well and 273 00:15:23,400 --> 00:15:24,720 Speaker 5: see where that happens. 274 00:15:25,080 --> 00:15:28,680 Speaker 1: So what recourse do you think will the government take here? 275 00:15:28,760 --> 00:15:32,000 Speaker 1: I mean, if we're talking about the property market really 276 00:15:32,040 --> 00:15:34,200 Speaker 1: being or the weakness of the property market being the 277 00:15:34,240 --> 00:15:38,000 Speaker 1: nerve center for this lack of positive centerment, this lack 278 00:15:38,040 --> 00:15:41,240 Speaker 1: of confidence in China. Is there anything the government can 279 00:15:41,320 --> 00:15:44,280 Speaker 1: can do to kind of fortify the weakness that we 280 00:15:44,320 --> 00:15:47,440 Speaker 1: have seen among these property developers or have they just 281 00:15:48,200 --> 00:15:50,480 Speaker 1: or are they allowing the markets to kind of have 282 00:15:50,600 --> 00:15:51,520 Speaker 1: their way right now? 283 00:15:53,280 --> 00:15:55,040 Speaker 5: I think that's a tough question, and I think that's 284 00:15:55,080 --> 00:15:59,600 Speaker 5: one that we continue to watch because last year the 285 00:15:59,600 --> 00:16:02,680 Speaker 5: government did put in kind of small amount inject small 286 00:16:02,720 --> 00:16:06,080 Speaker 5: amounts of liquidity into the market, right and this was 287 00:16:06,120 --> 00:16:09,840 Speaker 5: seen as marginally helping the property sector kind of show 288 00:16:09,960 --> 00:16:13,720 Speaker 5: up the very worst of it. But on the other hand, 289 00:16:14,240 --> 00:16:17,960 Speaker 5: the debt and kind of the magnitude is so great 290 00:16:18,160 --> 00:16:21,240 Speaker 5: that actually it's not enough. And the real question is 291 00:16:21,680 --> 00:16:24,600 Speaker 5: to what extent will the government continue to help the 292 00:16:24,640 --> 00:16:28,200 Speaker 5: property sector. And that's kind of the question that's kind 293 00:16:28,200 --> 00:16:30,640 Speaker 5: of been hanging over everyone for the past three years. 294 00:16:30,760 --> 00:16:32,840 Speaker 2: All Right, Kerrie, thanks very much for joining us. Kerry 295 00:16:32,840 --> 00:16:36,600 Speaker 2: Sue Lindberg, their private credit and loans reporter for Bloomberg. 296 00:16:37,120 --> 00:16:40,480 Speaker 2: We should mention that these finds huge fines that were 297 00:16:40,680 --> 00:16:45,520 Speaker 2: leveled against Huai Khayan are civil in nature. No criminal 298 00:16:45,600 --> 00:16:47,800 Speaker 2: charges have been filed against him, but as you mentioned, 299 00:16:47,960 --> 00:16:51,200 Speaker 2: he's being held under police control and seems to suggest 300 00:16:51,240 --> 00:16:54,760 Speaker 2: that there will be illegal crimes coming because when he 301 00:16:54,920 --> 00:16:58,200 Speaker 2: was put under this control, it was mandatory measures due 302 00:16:58,240 --> 00:17:01,880 Speaker 2: to quote suspicion illegal crime something to watch out for. 303 00:17:03,200 --> 00:17:06,119 Speaker 1: This has been the Bloomberg Daybreak Asia podcast, bringing you 304 00:17:06,200 --> 00:17:09,320 Speaker 1: the stories making news and moving markets in the Asia Pacific. 305 00:17:09,800 --> 00:17:12,920 Speaker 1: Visit the Bloomberg Podcast channel on YouTube to get more 306 00:17:12,960 --> 00:17:16,560 Speaker 1: episodes of this and other shows from Bloomberg. Subscribe to 307 00:17:16,600 --> 00:17:20,399 Speaker 1: the podcast on Apple, Spotify, or anywhere else you listen, 308 00:17:20,480 --> 00:17:23,600 Speaker 1: and always on Bloomberg Radio, the Bloomberg Terminal, and the 309 00:17:23,600 --> 00:17:24,680 Speaker 1: Bloomberg Business app.